Name
Abstract | ||
|---|---|---|
Urban area classification of Very High Resolution optical images relies on the one hand on the precise characterization of homogenous spectral responses within objects. On the other hand, sharp edges between the same objects, usual in man-made environments, have to be correctly detected. These two conflicting requirements make adaptive algorithms more suitable fo the task. The present work is devoted to introduce and validate one of these adaptive algorithms, based on Markov Random Fields (MRF) and neural networks. the approach works in a separate way on the two parts of the image, homogeneous and non.homogeneous ones, and allows to take into account their peculiarities. As such, it proves to be more reliable and accurate than basic maximum likelihood or even MRF and neural network classifiers considered alone. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/IGARSS.2007.4423091 | IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET |
Keywords | Field | DocType |
maximum likelihood,optical imaging,neural network,shape,maximum likelihood estimation,image resolution,remote sensing,neural nets,markov processes,neural networks,spatial resolution,image classification | Computer vision,Random field,Markov process,Pattern recognition,Computer science,Markov chain,Maximum likelihood,Artificial intelligence,Adaptive algorithm,Contextual image classification,Artificial neural network,Image resolution | Conference |
ISSN | Citations | PageRank |
2153-6996 | 1 | 0.36 |
References | Authors | |
4 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Giovanna Trianni | 1 | 90 | 10.92 |
| Paolo Gamba | 2 | 682 | 92.97 |